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Cross-Phenotype Association Analysis Using Summary Statistics from GWAS

  • Xiaoyin Li
  • Xiaofeng Zhu
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1666)

Abstract

For over a decade, genome-wide association studies (GWAS) have been a major tool for detecting genetic variants underlying complex traits. Recent studies have demonstrated that the same variant or gene can be associated with multiple traits, and such associations are termed cross-phenotype (CP) associations. CP association analysis can improve statistical power by searching for variants that contribute to multiple traits, which is often relevant to pleiotropy. In this chapter, we discuss existing statistical methods for analyzing association between a single marker and multivariate phenotypes, we introduce a general approach, CPASSOC, to detect the CP associations, and explain how to conduct the analysis in practice.

Key words

Genome-wide association studies Cross-phenotype association Meta-analysis Multivariate phenotypes Pleiotropy Summary statistics 

Notes

Acknowledgment

This work was supported by a grant from National Heart Genome Research Institute (HG003054).

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Copyright information

© Springer Science+Business Media LLC 2017

Authors and Affiliations

  1. 1.Department of Population and Quantitative Health SciencesSchool of Medicine, Case Western Reserve UniversityClevelandUSA

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